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Towards automated eye diagnosis: an improved retinal vessel segmentation framework using ensemble block matching 3D filter

Naveed, K, Abdullah, F, Madni, H A, Khan, M A U, Khan, Tariq and Naqvi, S S 2021, Towards automated eye diagnosis: an improved retinal vessel segmentation framework using ensemble block matching 3D filter, Diagnostics, vol. 11, no. 1, pp. 1-27, doi: 10.3390/diagnostics11010114.

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Title Towards automated eye diagnosis: an improved retinal vessel segmentation framework using ensemble block matching 3D filter
Author(s) Naveed, K
Abdullah, F
Madni, H A
Khan, M A U
Khan, TariqORCID iD for Khan, Tariq orcid.org/0000-0002-7477-1591
Naqvi, S S
Journal name Diagnostics
Volume number 11
Issue number 1
Article ID 114
Start page 1
End page 27
Total pages 27
Publisher MDPI AG
Place of publication Basel, Switzerland
Publication date 2021
ISSN 2075-4418
2075-4418
Keyword(s) Block Matching 3D (BM3D)
Diabetic Retinopathy (DR)
Vascular Endothelial Growth Factor (VEGF)
retina
speckle noise
Summary Automated detection of vision threatening eye disease based on high resolution retinal fundus images requires accurate segmentation of the blood vessels. In this regard, detection and segmentation of finer vessels, which are obscured by a considerable degree of noise and poor illumination, is particularly challenging. These noises include (systematic) additive noise and multiplicative (speckle) noise, which arise due to various practical limitations of the fundus imaging systems. To address this inherent issue, we present an efficient unsupervised vessel segmentation strategy as a step towards accurate classification of eye diseases from the noisy fundus images. To that end, an ensemble block matching 3D (BM3D) speckle filter is proposed for removal of unwanted noise leading to improved detection. The BM3D-speckle filter, despite its ability to recover finer details (i.e., vessels in fundus images), yields a pattern of checkerboard artifacts in the aftermath of multiplicative (speckle) noise removal. These artifacts are generally ignored in the case of satellite images; however, in the case of fundus images, these artifacts have a degenerating effect on the segmentation or detection of fine vessels. To counter that, an ensemble of BM3D-speckle filter is proposed to suppress these artifacts while further sharpening the recovered vessels. This is subsequently used to devise an improved unsupervised segmentation strategy that can detect fine vessels even in the presence of dominant noise and yields an overall much improved accuracy. Testing was carried out on three publicly available databases namely Structured Analysis of the Retina (STARE), Digital Retinal Images for Vessel Extraction (DRIVE) and CHASE_DB1. We have achieved a sensitivity of 82.88, 81.41 and 82.03 on DRIVE, SATARE, and CHASE_DB1, respectively. The accuracy is also boosted to 95.41, 95.70 and 95.61 on DRIVE, SATARE, and CHASE_DB1, respectively. The performance of the proposed methods on images with pathologies was observed to be more convincing than the performance of similar state-of-the-art methods.
Language eng
DOI 10.3390/diagnostics11010114
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30147389

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.